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Abstract

Until recently, neuroimaging data for a research study needed to be collected within one’s own lab. However, when studying inter-individual differences in brain structure, a large sample of participants is necessary. Given the financial costs involved in collecting neuroimaging data from hundreds or thousands of participants, large-scale studies of brain morphology could previously only be conducted by well-funded laboratories with access to MRI facilities and to large samples of participants. With the advent of broad open-access data-sharing initiatives, this has recently changed–here the primary goal of the study is to collect large datasets to be shared, rather than sharing of the data as an afterthought. This paradigm shift is evident as increase in the pace of discovery, leading to a rapid rate of advances in our characterization of brain structure. The utility of open-access brain morphology data is numerous, ranging from observing novel patterns of age-related differences in subcortical structures to the development of more robust cortical parcellation atlases, with these advances being translatable to improved methods for characterizing clinical disorders (see Figure 1 for an illustration). Moreover, structural MRIs are generally more robust than functional MRIs, relative to potential artifacts and in being not task-dependent, resulting in large potential yields. While the benefits of open-access data have been discussed more broadly within the field of cognitive neuroscience elsewhere (Gilmore et al., in press; Poldrack and Gorgolewski, 2014; Van Horn and Gazzaniga, 2013; Van Horn and Toga, 2014; Vogelstein et al., 2016; Voytek, 2016), as well as in other fields (Ascoli et al., 2017; Choudhury et al., 2014; Davies et al., 2017), this opinion paper is focused specifically on the implications of open data to brain morphology research.

Author Comment

Added more rationale for using brain morphology as a research approach, as well as more discussion of cautions and considerations related to using open-access data. The paper has also been generally expanded throughout.

Author Contributions

Data Deposition

Funding

CRM is supported by a fellowship from the Canadian Institutes of Health Research (FRN-146793). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Do you think the ENIGMA project could be mentioned, as it is a nice approaches to collect large-scale neuroimaging datasets and the first sets of ENIGMA studies are mainly based on structural MRI data?

Another point is, is it good to give a list of what kind of measures can be extracted from the structural MRI data?

I think ENIGMA falls just outside of the scope here, as the data sharing terms there are sufficiently more complicated than for the projects listed here. For the datasets mentioned here, data can be downloaded within just a few minutes or require only a very brief data-use application form.

For the 'kind of measures', do you mean like cortical thickness, surface area, gyrification, etc., or do you mean something else?

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